GitHub-Proxy/draco
1
0
Fork 0
mirror of https://git.mirrors.martin98.com/https://github.com/google/draco synced 2025-08-12 03:39:06 +08:00
Code Issues Packages Projects Releases Wiki Activity

Cleanup pred schemes for 2.2

Browse Source 
- Removed unused prediction mode from constrained multi.
- Removed unused prediction mode from geometric normal.
- Changed num_orientations from I32 to varUI32 in tex coords.
- Removed unused center_value from normal octahedron.
This commit is contained in:
Frank Galligan 2017-10-13 10:07:33 -07:00
parent 9c75f367f2
commit 9d04026855
1 changed files with 1 additions and 24 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

25
docs/spec/prediction.decoder.md
View File

@ -64,7 +64,6 @@ void ParseWrapTransformData() {
~~~~~
void ParseNormalOctahedronCanonicalizedTransformData() {
pred_trasnform_normal_max_q_val[curr_att_dec][curr_att] I32
temp I32
}
~~~~~
{:.draco-syntax}
@ -97,16 +96,6 @@ void ParsePredictionRansData() {
{:.draco-syntax}
### ParseConstrainedMultiMode()
~~~~~
void ParseConstrainedMultiMode() {
temp UI8
}
~~~~~
{:.draco-syntax}
### ParseConstrainedMultiNumFlags()
~~~~~
@ -121,7 +110,6 @@ void ParseConstrainedMultiNumFlags() {
~~~~~
void DecodePredictionData_ConstrainedMulti() {
ParseConstrainedMultiMode();
for (i = 0; i < kMaxNumParallelograms; ++i) {
ParseConstrainedMultiNumFlags();
if (constrained_multi_num_flags > 0) {
@ -144,7 +132,7 @@ void DecodePredictionData_ConstrainedMulti() {
~~~~~
void ParseTexCoordsNumOrientations() {
tex_coords_num_orientations I32
tex_coords_num_orientations varUI32
}
~~~~~
{:.draco-syntax}
@ -171,21 +159,10 @@ void DecodePredictionData_TexCoords() {
{:.draco-syntax}
### ParseGeometricNormalPredictionMode()
~~~~~
void ParseGeometricNormalPredictionMode() {
temp UI8
}
~~~~~
{:.draco-syntax}
### DecodePredictionData_GeometricNormal()
~~~~~
void DecodePredictionData_GeometricNormal() {
ParseGeometricNormalPredictionMode();
ParsePredictionRansData();
RansInitDecoder(ans_decoder_, data.data() + pos,
prediction_rans_data_size, L_RANS_BASE);